Unsupervised Extractive Summarization by Human Memory Simulation

TL;DR

This paper proposes an unsupervised extractive summarization method that uses heuristics inspired by human memory models to identify important content in long, structured documents, improving relevance and summary quality.

Contribution

It introduces novel heuristics based on cognitive representations of memory for content selection in unsupervised extractive summarization of scientific articles.

Findings

Heuristics effectively leverage cognitive structures and document organization.

Proposed methods outperform baselines in automatic evaluations.

Human assessments confirm improved relevance of extracted content.

Abstract

Summarization systems face the core challenge of identifying and selecting important information. In this paper, we tackle the problem of content selection in unsupervised extractive summarization of long, structured documents. We introduce a wide range of heuristics that leverage cognitive representations of content units and how these are retained or forgotten in human memory. We find that properties of these representations of human memory can be exploited to capture relevance of content units in scientific articles. Experiments show that our proposed heuristics are effective at leveraging cognitive structures and the organization of the document (i.e.\ sections of an article), and automatic and human evaluations provide strong evidence that these heuristics extract more summary-worthy content units.